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Interactive Dictionary Expansion using Neural Language Models
2018
International Semantic Web Conference
Dictionaries and ontologies are foundational elements of systems extracting knowledge from unstructured text. However, as new content arrives keeping dictionaries up-to-date is a crucial operation. In this paper, we propose a human-in-the-loop (HumL) dictionary expansion approach that employs a lightweight neural language model coupled with tight HumL supervision to assist the user in building and maintaining a domain-specific dictionary from an input text corpus. The approach is based on the
dblp:conf/semweb/AlbaGRW18
fatcat:dci5mvin7zhghk7ci6regnff6a